Brain computer interface (BCI) [J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, Brain-computer interfaces for communication and control, Clinical Neurophysiology, vol. 113, pp. 767-791, 2002.; E. A. Curran and M. J. Strokes, Learning to control brain activity: A review of the production and control of EEG components for driving brain-computer interface (BCI) systems, Brain and Cognition, vol. 51, pp. 326-336, 2003.] functions as a direct communication pathway between a human brain and an external device. As it directly uses the electrical signatures of the brain's activity for responding to external stimuli, it is particularly useful for paralyzed people who suffer from severe neuromuscular disorders and are hence unable to communicate through the normal neuromuscular pathway. The electroencephalogram (EEG) is one of the widely used techniques out of many existing brain signal measuring techniques due to its advantages such as its non-invasive nature and its low cost.
Farwell and Donchin [L. A. Farwell and E. Donchin, Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potential, Electroencephalography and Clinical Neurophysiology, vol. 70, pp. 510-523, 1988.] first demonstrated the use of P300 for BCIs in a so-called oddball paradigm. P300 is an endogenous, positive polarity component of the evoke-related-potential (ERP) elicited in the brain in response to infrequent/oddball auditory, visual or somatosensory stimuli. In the oddball paradigm, the computer displays a matrix of cells representing different letters, and flashes each row and column alternately in a random order. FIG. 1 shows an example of the matrix of cells 100 displayed by a computer in the oddball paradigm. A user trying to input a letter is required to pay attention to the target letter for a short while. In this process, when the row or column containing the intended letter flashes, a P300 will be elicited in the subject's EEG, which is then identified by using signal processing and machine learning algorithms.
One problem with using the P300 in BCIs is that large inter-subject variations exist among P300 of different subjects. For example, the P300 amplitude and latency vary among both normal and clinical populations. Such variations have been linked with individual differences in cognitive capability. Therefore, from the pattern recognition viewpoint, computational P300 classification models built for one subject does not accurately apply to another subject. To solve this problem, most P300-based BCIs usually first perform a special training session to learn a subject-specific classification model. In that special training session, a subject is required to follow instructions and focus on a particular cell visually at a given time while his or her EEG is being recorded. Subsequently, certain computer algorithms are implemented to perform the signal analysis and to learn a subject-specific classification model based on the recorded EEG. One problem with the special training session described above is that it is normally complicated and tedious, making most P300-based BCIs user-unfriendly. Furthermore, the requirement for the special training sessions makes the practical implementation of P300-based BCIs difficult.
Hence, in view of the above, there exists a need for a method and system for classifying brain signals in a BCI which seek to address at least one of the above problems